Advertising aims to induce changes in a consumer's emotional state in the real world, and to translate this change in emotional state into performance, such as sales lift. For example, a television commercial may look to increase sales of a product to which it relates. Real world effects may be any objectively measurable outcome that can be linked with consumption of the piece of media content. The outcome may be indicated by predicted performance data. Predicted performance data may relate, for example, to predicted sales lift (e.g., where the media content is an advertisement aimed at selling a particular product), or social media response (e.g., likelihood of going viral), or likelihood of winning an award (e.g., a Cannes Lions award for advertising). For example, performance data can be predicted based on properties of user emotional responses that are collected as a piece of media content is consumed. Over $80 billion is spent annually on television commercials in the US alone. There is therefore a large demand for being able to evaluate the effectiveness of media content prior to publication, by predicting performance.
One conventional option for measuring advertising performance effectiveness is to correlate a given piece of media content with sales performance. However, such correlation is done retrospectively, and comes with the problem of being blind to emotional state of consumers/users.
Another conventional option is to use active feedback, which is also referred to as self-reported feedback, which attempts to determine or predict the performance of pieces of media content, such as video commercials. For active user feedback, users provide verbal or written feedback after consuming a piece of media content. For example, the users may complete a questionnaire, or may provide spoken feedback that can be recorded for analysis, e.g., manually or in an automated manner using speech recognition tools. Feedback may include an indication of emotional state experienced while consuming the piece of media content.
In order for active feedback to be scalable to large sample sizes, and thus be worthwhile, the feedback format must be short, for example limited to yes-or-no answers. This precludes a real-time, i.e., second-by-second, account of experienced emotional state. It is therefore not possible using conventional active feedback techniques to collate representative emotional state data for large sample sizes using active feedback.
Also, active feedback from users pulls from rationalized, conscious thought processes, rather than the (passive) emotional state actually experienced. It has been shown that user preferences are outside of conscious awareness, and strongly influenced by passive emotional state. Media content performance therefore cannot be accurately predicted using active emotional state feedback.
Active feedback is an example of measuring user emotional state using self-reporting. Emotional state data can also be measured in a passive manner, e.g., by collecting data indicative of a user's behavioral or physiological characteristics, e.g., while consuming a piece of media. In practice, it can be desirable to use a combination of raw data inputs comprising behavioral data, physiological data and self-reported data in order to obtain emotional state information. A combination of raw data from two or three of the sources mentioned above may be useful in identifying “false” indicators. For example, if emotional state data derived from all three sources overlaps or is aligned, it gives more confidence in the obtained signal. Any inconsistency in the signal may be indicative of a false reading.
Physiological parameters can be good indicators of what emotional state is being experienced. Many physiological parameters are not consciously controllable, i.e., a consumer has no influence over them. They can therefore be used to determine the true emotional state of a user consuming a piece of media content, which can in principle be used to accurately predict media content performance. Examples of physiological parameters that can be measured include voice analysis, heartrate, heartrate variability, electrodermal activity (which may be indicative of arousal), breathing, body temperature, electrocardiogram (ECG) signals, and electroencephalogram (EEG) signals.
It is increasingly common for users to possess wearable or portable devices capable of recording physiological parameters of the type described above. This opens up the possibility that such physiological measurements may be scalable to large sample sizes, which may enable statistical variations (noise) to be removed so that correlation with media content performance can be seen.
The behavioral characteristics of a user may manifest themselves in a variety of ways. References to “behavioral data” or “behavioral information” herein may refer to visual aspects of a user's response. For example, behavioral information may include facial response, head and body gestures or pose, and gaze tracking.
In one example, facial responses can be used as passive indicators of experienced emotional state. Webcam video acquisition can be used to monitor facial responses, by capturing image frames as a piece of media content is consumed by a user. Emotional state can therefore be captured through the use of webcams, by processing video images.
Emotional state information measured in this way has been shown to correlate with media content performance, and in particular sales lift. The proliferation of webcams on client devices means that capture of this type of data can be scaled to large sample sizes.
However, even conventional passive techniques face various problems. Correlation between facial expression and media content performance has poor accuracy. It has been shown, for example, that the correlation of media content performance with facial expression can be higher than with active feedback, but only when the source data is significantly filtered. Content performance and facial expression correlation is also not applicable to every product category. Whilst these webcam-based techniques demonstrate a positive correlation between facial expression and media content performance, accuracy and consistency across product ranges is not achieved.
Therefore, there exists a need in the art to solve the problems of inaccuracy and inconsistency of evaluating consumer emotional state across different product categories, as well as the difficulty of large-scale data scaling, posed by conventional advertising performance evaluation techniques.